The twoblock clustering tree (\tbtree) is introduced as a highly interpretable regression tree for multivariate responses. Twoblock trees are deterministic decision trees that have local multivariate linear models as their leaves and use dense or sparse twoblock dimension reduction as local leaf models and in the impurity. The resulting models are both computationally efficient and can be highly interpretable. The estimator's primary aim is an interpretable, regime-aligned piecewise-linear description of the data, with predictive competitiveness retained as a constraint through a split/leaf decoupling. Beyond proposing the decision tree estimator itself, this paper also introduces an estimator for the twoblock dimension reduced space based on maximizing coskewness, which facilitates identification of non-normal clusters in the data. The tree inherently produces a set of local linear models and is therefore apt to recover piecewise linear regimes, which is illustrated in a simulation. However, two real-world data examples illustrate that twoblock trees are also capable of modeling more complexly nonlinear dependencies and can perform on par with black-box modeling techniques, such as random forests. At each point, both the twoblock models that generate the splits, as well as the ones in the leaves, can be inspected and interpreted.

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